WUSTL High-Definition Video Trace Library

Presented here are
our results of encoding more than 50 high
definition (HD) video traces collected from the popular web video
streaming website: YouTube. Additionally, we provide a simple
statistical analysis of the video traces and support it with different
graphs. As a part of this contribution, we evaluate the capability of
the SAM model [JSAC10, MMSys10] of representing these traces accurately.
SAM parameters for each movie traces is then calculated and presented.
Our approach can be used not only to model and generate different video
traces but also to predict resource demands and to optimize scheduling
and network coding in real time. The figure blow shows the steps we took
in analyzing and modeling the selected videos with their corresponding
outputs.

Modeling, analyzing, and generating video traces processes

The process starts with a YouTube video, which is then converted to a
YUV raw video. The raw video is consequently encoded with AVC to produce
an encoded movie file, an encoding statistics file, and a full verbose
description of the encoding process. The verbose output is then parsed
to get the video frames information, which is then modeled using SAM.
The video frames' vector is used to produce the autocorrelation function
(ACF) and the partial autocorrelation function (PACF) graphs. The
video's SAM parameters then can be used either to predict future
traffic, or to produce a movie trace. To produce a trace, SAM frame
generator uses these parameters to generate a movie trace that is
statistically close to the original movie trace.